Development and validation of satellite-derived surface NO <sub>2</sub> estimates using machine learning versus traditional approaches in North America
Bibliographic record
Abstract
Abstract. Nitrogen dioxide (NO2) is one of the key pollutants with profound implications for air quality, and human health, and is needed to establish the air quality health index (AQHI). Currently, over 600 surface air monitoring stations are distributed across Canada and the United States measuring NO2, but many areas remain unmonitored leading to incomplete information for health risk assessments. This study leverages Tropospheric Monitoring Instrument (TROPOMI) satellite observations and machine learning models to derive high-resolution surface NO2 concentrations, provides enhanced spatial coverage and accuracy, revealing urban-rural NO2 gradients across North America. Existing traditional methods rely on scaling with modeled profiles to obtain NO2 surface concentrations from satellite observations. Here, we compare this traditional method to a machine learning approach that utilizes NO2 observations from TROPOMI, together with meteorological parameters, land cover type, topography, and emission inventories. Our results show that the machine learning (using random forest) yields less bias between the surface monitoring measurements and the "satellite-derived" surface concentrations, significantly improved the correlation coefficient (R2~0.77–0.91) compared to the traditional method (R2~0.39–0.57) and yields to significantly less bias.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".